416results about "Auscultation instruments" patented technology

The invention relates to a ventilation tube (1) for ventilating a patient, comprising a tube (2) surrounding an air channel (25) with a tube wall (3) for insertion into a throat or trachea of the patient, a cuff (4, 15) with an air volume (6) circulating in the circumferential direction of the tube wall (3), and a pump line (5) running in the axial direction of the tube (2) for establishing a flow connection between the air volume (6) of the cuff (4, 15) and a pump device (7). In order to be able to detect conditions relating in particular to the breathing or ventilation of the patient with high quality, it is proposed that the ventilation tube (1) have an acoustic sensor (8, 16) arranged in the air volume (6) on the tube wall (3) or in a partial region of the cuff (4, 15) adjacent to the air channel (25).

Obstructive sleep apnea from blockage of the upper airway can result in significant health issues. Described herein is an intelligent personalized closed loop neuromodulation system and methods to prevent backwards movement of the genioglossus muscle through stimulation of certain branches of the hypoglossal nerve and muscle motor points, by using muscle feedback from electromyogram sensors to provide optimal stimulus. Alternative embodiments to treat neuropathic pain and urinary dysfunction are enclosed.

Coaptation assist device for repairing cardiac valves and associated systems and methods are disclosed herein. A coaptation assist device configured in accordance with embodiments of the present technology can include, for example, a fixation member configured to press against cardiac tissue proximate to a native valve annulus, and a stationary coaptation structure extending away from the fixation member. The coaptation structure can include an anterior surface configured to coapt with a first native leaflet during systole and a posterior surface configured to displace at least a portion of a second native leaflet. The device also includes at least one sensor configured to detect parameters associated with at least one of cardiac function and device functionality. The sensors can be pressure sensors configured to detect left atrial pressure and / or left ventricular pressure.

The invention discloses an intelligent atomization control method and system for respiratory tract treatment and a storage medium, and relates to the technical field of atomization control. During operation of the system, a multi-mode patient respiratory behavior monitoring module is used for capturing real-time respiratory frequency, depth, rhythm, cough characteristics and atomization efficacy based on a non-contact sonic sensor and lung sound analysis; the method comprises the following steps: monitoring environment humidity, temperature and aerosol diffusion rate in real time, analyzing the influence of the environment humidity, temperature and aerosol diffusion rate on drug mist particle size and sedimentation behavior, controlling atomization particle size through a variable-frequency ultrasonic exciter, calculating to obtain an intelligent atomization curative effect adaptation index AEAI, dynamically adjusting particle size distribution according to the real-time inhalation demand of a patient and environment parameters, and combining with respiration monitoring data to obtain the intelligent atomization curative effect adaptation index AEAI. According to the method, the medicine mist release opportunity is adjusted in real time, threshold value comparison is carried out by monitoring respiratory resistance changes and real-time feedback of lung sound characteristic improvement degree, a medicine release strategy is obtained, and remote doctor viewing and parameter adjusting functions are provided.

A diagnostic system includes one or more support surfaces, on which a person to be examined can be positioned. To be able to make a diagnosis without specialist staff, electrodes are provided to be arranged on various regions of the support surface, particular on leg regions, on arm regions, on an upper body region and / or on a neck region. These electrodes are at least partly embodied as electrode pairs, wherein each electrode pair has a feed electrode and / or a measurement electrode. The feed electrode is arranged distally relative to the measurement electrode. A device is provided for feeding an electric current between the feed electrodes and the diagnostic system is configured to measure a voltage drop between the measurement electrodes in order to be able to determine impedances of body regions of a person located on the diagnostic system.

A method of identifying a patient with obstructive sleep apnea implants a phrenic nerve stimulator in the patient or connects the phrenic nerve stimulator to the diaphragm, and adjusts the stimulation energy applied by the phrenic nerve stimulator to the phrenic nerve of the sleeping patient based on airway occlusion in the airway of the patient.

The invention discloses an abnormal data detection method, device and system based on electrocardio and heart sound, and the method comprises the steps: synchronously collecting an electrocardio signal and a heart sound signal, and carrying out the first preprocessing and the second preprocessing, thereby obtaining electrocardio data and heart sound data; inputting the electrocardio data and the heart sound data into a double-flow convolutional network for feature extraction to obtain an electrocardio feature sequence and a heart sound feature sequence; inputting the electrocardio feature sequence and the heart sound feature sequence into a cross-modal fusion decision model to obtain pooling fusion enhanced features, thereby effectively performing time sequence alignment, fusion and enhancement on the electrocardio feature sequence and the heart sound feature sequence; the score of the pooling fusion enhanced feature is obtained by using a nonlinear scoring formula, whether abnormal data exist in the electrocardiosignal and the heart sound signal of the detected person or not is determined through the score of the pooling fusion enhanced feature, and the method has important significance on early warning, curative effect evaluation and the like of patients with cardiovascular and cerebrovascular diseases.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

An anti-snoring system comprising an adjustable bed having a sleeping surface that may be mechanically raised or lowered, and a control module adapted to receive commands from a source external to the adjustable bed; a mobile device in direct or indirect communication with the control module in the adjustable bed, and wherein the mobile device has sound recording capabilities; a mobile application resident on the mobile device, wherein the mobile application includes a sound classification machine learning model that includes an artificial intelligence or neural network operative to determine whether or not a person on the sleep surface is snoring, and wherein upon a determination that the person is snoring and has been snoring for a predetermined period of time, the mobile application instructs the control module to raise or adjust the sleeping surface to a height or position that will discourage the person from snoring.

The invention relates to the technical field of respiratory lung sound recognition, in particular to a respiratory lung sound auxiliary recognition method and system for clinical nursing. The invention provides a respiratory lung sound auxiliary identification method and system for clinical nursing, solves the technical problems of data scarcity, complex noise interference and insufficient cross-device generalization ability in lung sound signals through generative data enhancement and cross-modal migration, and combines self-supervised comparative learning and causal feature discovery to identify the respiratory lung sound. Multi-source signal feature fusion and noise robustness improvement are realized, a real-time noise environment is dynamically adapted, the limitation that a traditional method depends on annotation data and feature extraction is easily influenced by hybrid noise is broken through, a full-link closed-loop system from synthetic data generation, cross-modal alignment to causal-driven decision is constructed, and multi-source signal feature fusion and noise robustness improvement are realized. And the accuracy and clinical applicability of clinical lung sound analysis are effectively improved.

The invention relates to the technical field of mattresses, in particular to a multifunctional heart failure patient condition monitoring mattress which comprises a mattress body. The mattress further comprises an adjusting mechanism, a monitoring mechanism and an analysis processing module, and the adjusting mechanism, the monitoring mechanism and the analysis processing module are all mounted on the mattress main body; the monitoring mechanism is used for monitoring the physical condition of a patient, the adjusting mechanism is used for adjusting the heights of different positions of the mattress main body, and the analyzing and processing module is used for analyzing and processing data acquired by the monitoring mechanism; a patient lies on the mattress body, the physical condition of the patient is monitored through the monitoring mechanism, then data obtained by the monitoring mechanism is analyzed and processed through the analysis and processing module, and the adjusting mechanism is controlled to adjust the heights of different positions of the mattress body, so that the shape of the mattress body is suitable for the patient, and the return blood volume is reduced; the comfort of the patient is improved, and the morbidity of the patient is reduced.

An electronic device may monitor, using a microphone, feeding sounds associated with an individual. Then, the electronic device may analyze the monitored feeding sounds, where the analysis uses a time-series shotgun sequencing technique. Next, the electronic device may detect, using a pretrained predictive model, a potential aerodigestive disorder based at least in part on the analyzed monitored feeding sounds. Note that the analyzed monitored feeding sounds may correspond to suck-swallow-breathe (SSB) coordination. Alternatively or additionally, the analyzed monitored feeding sounds may correspond to aspiration or dysfunctional phases of swallowing.

The invention relates to a postoperative gastrointestinal function monitoring method integrating flexible sensing and AI, and the method comprises the steps: placing a flexible electronic skin sensor in the abdominal regions of a plurality of patients, carrying out the collection of abdominal acoustic signals, obtaining a plurality of abdominal acoustic signals, and constructing a data set; preprocessing a plurality of samples in the data set, and dividing the preprocessed data set into a training set and a test set; constructing a gastrointestinal function monitoring model, and inputting the training set into the gastrointestinal function monitoring model for training; inputting the test set into the gastrointestinal function monitoring model obtained after training to judge the gastrointestinal peristalsis state; the method is used for assisting a doctor to know the gastrointestinal function state of a patient in real time, a flexible electronic skin sensor is adopted to be attached to the abdomen to collect signals, intubation or radiation examination is avoided, and the pain and infection risk of the patient are reduced; by means of artificial intelligence, the gastrointestinal function monitoring model is adopted to efficiently monitor the gastrointestinal peristalsis state in real time, and doctors are assisted in formulating a treatment scheme.

The invention provides a two-channel heart and lung sound intelligent analysis method based on time-frequency feature fusion. The method is used for solving the problem that in the prior art, the heart and lung sound signal analysis recognition capacity is insufficient. According to the method, the time-frequency features of the heart and lung sound signals of the patient are fused, and a plurality of feature extraction methods are combined, so that the accuracy of an analysis result is remarkably improved. The method is characterized by comprising the following steps of S1, performing data analysis on heart and lung sound signals through two feature extraction methods to obtain two feature images with time domain features and frequency domain features of heart and lung sound; s2, transmitting the two feature images with the time domain features and the frequency domain features of the heart and lung sounds to a dual-channel heart and lung sound intelligent analysis model; and S3, the two-channel cardiopulmonary sound intelligent analysis model performs dynamic feature fusion based on the two feature images with the cardiopulmonary sound time domain features and the cardiopulmonary sound frequency domain features, and obtains a cardiopulmonary sound analysis result based on a fusion result.

The present disclosure includes a medical monitoring hub as the center of monitoring for a monitored patient. The hub includes configurable medical ports and serial ports for communicating with other medical devices in the patient's proximity. Moreover, the hub communicates with a portable patient monitor. The monitor, when docked with the hub provides display graphics different from when undocked, the display graphics including anatomical information. The hub assembles the often vast amount of electronic medical data, associates it with the monitored patient, and in some embodiments, communicates the data to the patient's medical records.

The invention relates to a system and a method for determining a severity of a respiratory disease of a patient. The patient is instructed by a system to recite a phrase. The system receives the phrase recited by the patient as acoustic signal and analyzes the acoustic signal with respect to a severity of a respiratory disease. The result of the analysis is provided, which is indicative for the severity of the respiratory disease of the patient.

The present application relates to the technical field of health monitoring, in particular to a kind of infant asphyxia risk autonomous monitoring method and system based on laryngeal respiratory sound, comprising: utilize high sensitivity MEMS microphone to collect the airflow and respiratory sound signal of infant larynx, and utilize micro accelerometer to monitor the thoracic movement data of infant synchronously;The airflow and respiratory sound signal are denoising, and the denoising respiratory sound signal is obtained;The denoising respiratory sound signal is separated according to respiratory event Signal, and is enhanced and combined into respiratory sound data set;Respiratory sound feature is extracted in respiratory sound data set, and asphyxia anomaly classification model is constructed based on respiratory sound feature by machine learning model, and dynamic baseline model that adapts individual respiratory difference is established according to respiratory sound feature.The present application utilizes the hardware device of high sensitivity MEMS microphone and micro accelerometer and artificial intelligence model to combine, realizes the use of laryngeal respiratory sound signal in home environment and autonomously completes infant asphyxia risk identification and early warning.

Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and / or passive sensing. The processor(s) (7304, 7006) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and / or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing. Some versions may evaluate sensing signal(s) to generate indication(s) of cough event(s) and / or cough type.

The invention relates to the technical field of audio signal processing. The invention provides a method, system and device for predicting pulmonary nodules through sound and a storage medium. The method comprises the following steps: in a quiet test environment, directionally collecting a sound signal of a patient through an audio sensor; wherein the sound signals comprise breath sounds, cough sounds, vowels and sentences; performing noise reduction processing on the collected sound signals to obtain a noise reduction data set; extracting multi-dimensional voiceprint features from the noise reduction data set, and constructing a sound feature matrix; and performing multi-modal fusion analysis on the sound feature matrix and clinical data of the patient, inputting the sound feature matrix and the clinical data into a sound prediction model optimized by a focus loss function, and outputting a pulmonary nodule risk prediction result and a visual diagnosis report. The problems that the misdiagnosis and missed diagnosis rate is high, the radiation risk is large and the equipment dependence is strong when the pulmonary nodules are examined by the existing iconography, and the characteristic analysis is single, the noise immunity is poor and the screening accuracy is insufficient in the sound detection technology are solved.

According to some embodiments, an ear-worn device, e.g., a hearing aid, is provided that operates to monitor the health of a wearer of the ear-worn device based on an audio signal detected by the ear-worn device. In some embodiments, a method of monitoring the health of the wearer of the ear-worn device includes: detecting the audio signal with a microphone of the ear-worn device; processing the detected audio signal using a processor of the ear-worn device; and outputting, from an output signal generator of the ear-worn device, a generated output audio signal. In some embodiments, processing the detected audio signal includes: extracting data indicative of a health event associated with the wearer by processing the detected audio signal with a machine learning model; generating the output audio signal based on the detected audio signal; and outputting the data indicative of the health event.

Disclosed are devices, systems and related methods for wearable devices that sense various anatomical conditions and / or metrics of a wearer, including measuring and optionally transmitting this data to a computing device which analyzes the data to determine various physiological conditions within and / or in proximity to a wearer's body. The device comprises a central body having a first sensor and a body piercing post with a second sensor, wherein the sensors measure a first and second physiological condition respectively of the wearer continuously or periodically using specified time intervals.

A system for monitoring medical conditions including pressure ulcers, pressure-induced ischemia and related medical conditions comprises at least one sensor adapted to detect one or more patient characteristic including at least position, orientation, temperature, acceleration, moisture, resistance, stress, heart rate, respiration rate, and blood oxygenation, a host for processing the data received from the sensors together with historical patient data to develop an assessment of patient condition and suggested course of treatment, including either suspending or adjusting turn schedule based on various types of patient movement. The sensor can include bi-axial or tri-axial accelerometers, as well as resistive, inductive, capacitive, magnetic and other sensing devices, depending on whether the sensor is located on the patient or the support surface, and for what purpose.

The invention discloses a recurrence risk prediction method and device for an inflammatory bowel disease, and a server side method comprises the steps: obtaining and preprocessing historical clinical data and historical borborygmus data of a to-be-predicted object in a preset time period, and obtaining to-be-analyzed feature data; inputting the to-be-analyzed feature data into a pre-trained recurrence risk prediction model, and outputting a recurrence risk score of the inflammatory bowel disease corresponding to the to-be-predicted object; wherein the pre-trained recurrence risk prediction model is obtained by training based on collected target clinical data and target borborygmus data of each target object with the inflammatory bowel disease within a period of time; when the recurrence risk score is greater than or equal to a preset threshold value, generating early warning information indicating that the to-be-predicted object has the recurrence risk of the inflammatory bowel disease; and sending the early warning information to the client. Therefore, by adopting the embodiment of the invention, the recurrence risk of the inflammatory bowel disease patient can be effectively identified.

A method comprising to analyze the captured video data of the patient's body parts and to generate patient motion metrics, patient posture metrics, and patient gait metrics for each exercise routine completed by the patient based on the analyzed video data; store, in a database, the generated patient motion metrics, the patient posture metrics and the patient gait metrics for each of the completed exercise routines, wherein the generated patient motion metrics, the generated patient posture metrics and the generated patient gait metrics are used to track patient progress during physical therapy sessions, diagnose movement disorders, and provide personalized, data-driven treatment plans to enhance immediate outcomes and long-term recovery with respect to the musculoskeletal and neurological disorders; apply motion amplification algorithms to enhance the additional video data to provide clarity of tremor movements; apply edge detection to enhance tremor movement boundaries and detection; generate tremor amplitude measurements and tremor frequency measurements based on the motion amplification and edge detection algorithms; and store, in the database, the generated tremor amplitude measurements and tremor frequency measurements, wherein the generated tremor amplitude measurements and tremor frequency measurements are analyzed for diagnosis and monitoring of disorders such as Parkinson's disease and Essential Tremor.

The invention discloses a physiological sound separation and extraction method, and relates to the technical field of body sound signal processing and analysis. Comprising the steps of collecting human body physiological sound signals, constructing a multi-level signal separation model based on statistical characteristics, sequentially realizing sound signal elimination and extraction of various physiological sound signals, establishing a multi-dimensional characteristic parameter set, and performing intelligent identification and classification on the extracted physiological sound signals in combination with a deep learning classifier. And carrying out physiological and pathological state evaluation according to an identification and classification result. By comprehensively applying various signal processing and classification algorithms, automatic separation and accurate classification of heart sound, lung sound and borborygmus signals are realized, the physiological sound signal processing efficiency and precision are remarkably improved, and clinical doctors can be helped to quickly judge diseased regions and diseased property.

The invention relates to the technical field of health monitoring, in particular to an infant suffocation risk autonomous monitoring method and system based on laryngeal breathing sound, and the method comprises the steps: collecting airflow and breathing sound signals of the throat of an infant through a high-sensitivity MEMS microphone, and synchronously monitoring thoracic motion data of the infant through a miniature accelerometer; performing noise reduction processing on the airflow and the breathing sound signal to obtain a denoised breathing sound signal; performing signal separation on the denoised breathing sound signals according to a breathing event, and enhancing and combining the denoised breathing sound signals into a breathing sound data set; extracting breath sound features from the breath sound data set, constructing a suffocation anomaly classification model based on the breath sound features through a machine learning model, and establishing a dynamic baseline model adapted to individual breath differences according to the breath sound features; hardware devices of a high-sensitivity MEMS microphone and a micro accelerometer are combined with an artificial intelligence model, and infant asphyxia risk identification and early warning are autonomously completed in a home environment by using laryngeal respiration sound signals.

The invention provides a pediatric respiratory disease diagnosis system and method based on pattern recognition. The pediatric respiratory disease diagnosis method comprises the following steps: constructing a multi-modal pathological space by combining pathophysiological features of children in various medical institutions; performing breathing mode matching on the breathing time sequence data of the target patient and the subtype distribution of the breathing disease in the multi-mode pathological space to obtain pathological state constraints, and determining a pathological association relationship between the target patient and the known pediatric breathing disease according to the pathological state constraints and gray texture features of the lung medical image of the target patient; constructing a disease progress evolution graph of the pediatric respiratory disease; and carrying out map fusion on the disease progress evolution map and the pathology association relationship to obtain a multi-dimensional decision index of the respiratory disease of the target patient, and carrying out multi-modal identification on the respiratory disease of the target patient based on the multi-dimensional decision index to obtain a disease auxiliary identification report. By adopting the scheme of the invention, a comprehensive diagnosis system considering multi-center feature alignment and multi-modal information fusion can be constructed to identify pediatric respiratory diseases.

The invention discloses a multi-modal electrocardiosignal and arrhythmia recognition system and method. The system comprises a signal acquisition module, a preprocessing module, a space-time alignment module, a feature extraction module, a self-adaptive fusion module, a multi-modal fusion module, a classification risk assessment module and a visual report module. The recognition method comprises the following steps: S1, synchronously acquiring an electrocardiosignal and a heart sound signal, and preprocessing the electrocardiosignal and the heart sound signal; s2, establishing a time corresponding relation between the electrical activity and the mechanical activity; s3, calculating a mechanical shrinkage efficiency index; s4, evaluating the quality of the electrocardiosignal and the heart sound signal in real time, and dynamically adjusting contribution weights of the electrocardiosignal and the heart sound signal in a fusion process according to the quality; s5, adopting a multi-head attention mechanism to generate a joint feature vector for classification; s6, inputting the joint feature vector into a classification network model, and outputting an arrhythmia type and a quantitative risk level at the same time by the classification network model; and S7, generating a visual atlas and a structured diagnosis report.

The novel systems and methods use features extracted from a patient's breath audio signal acquired during periods of full wakefulness to classify patients as having or not having obstructive sleep apnea using machine learning algorithms. In some embodiments, the features are sequentially preprocessed and then modeled. Models with high correlation to the response and low overlap percentage are used in the classification process.

A device for cardiac monitoring, comprising a processor having a digital signal processing unit, the digital signal processing unit configured to receive and process physiological signals from at least one sensor assembly having one or more sensors, the processor including a program having executable instructions configured, when executed on the processor, to cause the processor to perform the following steps: synchronizing the processed signals of each sensor assembly, mapping the synchronized signals as waveforms of each sensor, calculating at least one cardiac function parameter as data values ​​with predetermined waveform amplitudes and predefined time intervals, and performing a step of differential analysis between the calculated data values ​​and a set of reference cardiac health parameters to determine a cardiac condition.